Prof. Heming Jia, Sanming University, China

Speech Title: Swarm Intelligence Optimization Algorithms and Engineering Application (Abstract)

LiProfessor Jia Heming is an outstanding academic talent at Sanming University. He holds a doctoral degree in Systems Engineering from Harbin Engineering University and has completed postdoctoral research in Mechanical Engineering. He has been selected into the list of the world's top 2% scientists (Stanford List) and the "2024 CNKI China's Top 1% Highly Cited Scholars" list. At the local level, he is rated as an Excellent Teacher in Sanming City.​ In the academic community, Professor Jia is actively involved in various activities. He is a council member of the Fujian Artificial Intelligence Society and the Fujian Computer Society. He also holds important editorial positions in several academic journals. He is an editorial board member of the SCI-indexed journal "Biomimetics", an editorial board member of the EI - indexed journal "Information", an assistant editorial board member of the Chinese core journal "CAAI Transactions on Intelligent Systems", a young editorial board member of the Chinese core journal "Journal of Frontiers of Computer Science and Technology". He is the director of the Artificial Intelligence and Information Fusion Application Innovation Research Center of Sanming University, a "Golden Phoenix Young Scholar" of Sanming University, and has been awarded the title of "My Favorite Teacher" of Sanming University. In terms of scientific research achievements, as the first author and corresponding author, he has published more than 100 scientific research papers, among which more than 70 are included in SCI, and 7 papers are selected as ESI highly cited papers. He has proposed original intelligent optimization algorithms, such as the Remora Optimization Algorithm, the Crayfish Optimization Algorithm, the Catch Fish Optimization Algorithm, and the Superb Fairy-wren Optimization Algorithm. His research results cover fields such as intelligent optimization algorithms, multi-objective evolutionary algorithms, machine learning, and feature selection, and have had an important impact on related academic and technical fields.

Prof. Yahui Peng, Beijing Jiaotong University, China

Yahui Peng received the B.E. degree in Engineering Physics from Tsinghua University, Beijing, China, in 1998, the M.E. degree in Nuclear Technology and Applications from Tsinghua University, Beijing, China, in 2001, and the Ph.D. degree in Medical Physics from the University of Chicago, Chicago, IL, USA, in 2010. Currently, he is a full Professor affiliated with the School of Electronic and Information Engineering at Beijing Jiaotong University, Beijing, China. He has extensive research experience in computer-aided diagnosis of prostate cancer in MR images, supported by grants from National Institutes of Health, Department of Defense and Natural Science Foundation of China. He has published more 90 peer-reviewed journal papers.

Dr. Na Liu, Tianjin Normal University, China

Liu Na received her PhD from the School of Mathematics, Harbin Institute of Technology in 2021. From September 2021 to September 2023, she worked as a postdoctoral researcher in the Department of Automation at Tsinghua University. She is currently employed at the School of Mathematical Sciences, Tianjin Normal University. Her research focuses on neurodynamic optimization theory and multi-agent distributed optimization. She leads a sub-topic of a national key R&D program and a joint fund project of the Tianjin Natural Science Foundation. As a core member of the research team, she has participated in topics of national key R&D programs, as well as multiple projects of the National Natural Science Foundation and open fund projects of national key laboratories. To date, she has published more than 10 high-level academic papers in international journals such as IEEE Transactions on Cybernetics (IEEE TCYB), IEEE Transactions on Automatic Control (IEEE TAC), Neural Networks, and Neurocomputing.

 

Speech Title: Neural Dynamics Optimization Algorithm for Nonsmooth Nonconvex Optimization Problems

Speech Abstract: Given that many real-world complex systems and models inherently possess nonsmooth and nonconvex characteristics, nonsmooth nonconvex optimization problems hold significant importance in engineering practice. However, the nonsmoothness and nonconvexity of the objective function and constraint functions pose substantial challenges to the design and convergence analysis of optimization algorithms. To address such optimization difficulties, this paper proposes a novel smooth gradient approximation neural network. In this network, we innovatively introduce a smoothing approximation technique with time-varying control parameters, aiming to effectively handle nonsmooth and nonregular objective functions. Furthermore, to ensure that the state solution of the proposed neural network remains within the nonconvex inequality constraint set, a hard comparator function is introduced, and it is further proven that any accumulation point of the constructed neural network's state solution is a stationary point of the studied nonconvex optimization problem. Notably, this neural network also demonstrates strong capability in finding optimal solutions when solving certain generalized convex optimization problems. Compared with existing related neural networks, the neural network proposed in this paper has more relaxed convergence conditions and a simpler algorithm structure. Through simulation experiments and practical applications in terms of optimization condition numbers, the practicality and effectiveness of the proposed algorithm are fully verified.